Syllabus, Master's level, 1TD389
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Computational Science A1N, Computer Science A1N, Technology A1N
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 11 November 2015
- Responsible department
- Department of Information Technology
120 credits including Computer Programming I and Scientific Computing II. Scientific Computing II may be replaced by Numerical Methods and Simulation, 5 credits, Scientific Computing, Bridging Course, 5 credits, or Scientific Computing and Calculus, 10 credits.
To pass, the student should be able to
- describe the data flow in a visualisation system;
- outline the methods that transform the data and information to visual representations;
- use and program advanced software for various visualisation techniques.
Scientific Visualisation is an area concerned with the visualisation of large and complex data sets, where the data might come from experiments or computations. Visualisation is a way, in many cases the only possible way, to achieve insight and knowledge.
Discrete models. Volume rendering: ray-tracing, splatting, texture based. Isosurface reconstruction. Transformation of discrete volume data to polygonal representations. Mesh topologies and mesh simplification. Visualisation techniques. Visual aspects based on perception. Particle rendering. Algorithms for programmable graphics hardware. Applied visualisation. The course includes projects such as programming in VTK (the Visualisation Toolkit).
Lectures, laboratory work and compulsory assignments.
Written examination at the end of the course. Passed laboratory course and approved compulsory assignments are also required.